Spoken term discovery. This task consists in searching audio recordings for speech sequences that are similar in acoustic space. One popular method is to use Segmental DTW [1]} on hand-crafted features, mainly MFCC and PLP, to find subparts of two utterances that have a good DTW alignment score. The main problem with this method, as shown by [2]}, is that for some unknown reason, even though it works well on traditional hand-crafted speech features like MFCC and PLP features, it fails to benefit from the theoretically higher quality frame-level self-supervised embeddings like Wav2vec 2.0, CPC or Hubert. Another method which does not share this limitation, has been proposed by [3]}. It works by encoding all speech sequences of a corpus of a given length range with an SSE and look for pairs with low cosine distance using accelerated k-Nearest Neighbors search. We adopt this method here, and the NED/COV metric from the Zerospeech challenges [4]}
to evaluate spoken term discovery.
